Abstract

Given the characteristics of the COVID-19 pandemic and the limited tools for orienting interventions in surveillance, control, and clinical care, the current article aims to identify areas with greater vulnerability to severe cases of the disease in Rio de Janeiro, Brazil, a city characterized by huge social and spatial heterogeneity. In order to identify these areas, the authors prepared an index of vulnerability to severe cases of COVID-19 based on the construction, weighting, and integration of three levels of information: mean number of residents per household and density of persons 60 years or older (both per census tract) and neighborhood tuberculosis incidence rate in the year 2018. The data on residents per household and density of persons 60 years or older were obtained from the 2010 Population Census, and data on tuberculosis incidence were taken from the Brazilian Information System for Notificable Diseases (SINAN). Weighting of the indicators comprising the index used analytic hierarchy process (AHP), and the levels of information were integrated via weighted linear combination with map algebra. Spatialization of the index of vulnerability to severe COVID-19 in the city of Rio de Janeiro reveals the existence of more vulnerable areas in different parts of the city’s territory, reflecting its urban complexity. The areas with greatest vulnerability are located in the North and West Zones of the city and in poor neighborhoods nested within upper-income parts of the South and West Zones. Understanding these conditions of vulnerability can facilitate the development of strategies to monitor the evolution of COVID-19 and orient measures for prevention and health promotion.

Four months after the emergence and spread of the novel coronavirus (SARS-CoV-2) to numerous countries around the world, the impact from the number of confirmed cases (823,626) and deaths (40,598), alongside the enormous pressure on health systems due to the need for hospital care of severe cases, poses one of the most daunting global health challenges in recent decades 11. World Health Organization. Novel Coronavirus (2019-nCoV) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (acessado em 02/Abr/2020).https://www.who.int/emergencies/diseases... ,22. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20:533-4..

On April 1st, 2020, the world reported more than 4,800 deaths from COVID-19. The death toll exceeded 5,000 by the following day 11. World Health Organization. Novel Coronavirus (2019-nCoV) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (acessado em 02/Abr/2020).https://www.who.int/emergencies/diseases... ,22. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20:533-4.. The rapid rise in the number of deaths makes COVID-19 the deadliest infectious disease in the world, surpassing tuberculosis (TB), which killed approximately 4,000 people a day in 2018 according to the World Health Organization (WHO) 33. World Health Organization. Global tuberculosis report 2019. https://www.who.int/tb/publications/global_report/en/ (acessado em 02/Abr/2020).https://www.who.int/tb/publications/glob... .

Differential risk of COVID-19 distribution is suggested by the biological characteristics of SARS-CoV-2, with high infectivity 44. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020; 395:931-4. and occurrence of the infection in a completely susceptible population occupying extremely heterogenous territories in terms of living conditions. The groups at greatest risk of developing the severe form of COVID-19 are the elderly, individuals with preexisting respiratory diseases and debilitated immune systems, and population groups living in crowded conditions 55. Organização Pan-Americana da Saúde. Folha informativa - Covid-19. https://www.paho.org/bra/index.php?option=com_content&view=article&id=6101:covid19&Itemid=875 (acessado em 02/Abr/2020).https://www.paho.org/bra/index.php?optio... .

In the city of Rio de Janeiro, Brazil, starting with the introduction of the virus until maintenance of the circulation of autochthonous cases, growing numbers of confirmed cases and deaths have been reported (1,110 cases and 47 deaths as of April 6, with an estimated 4.24% case-fatality rate (Painel Rio COVID-19. https://experience.arcgis.com/experience/38efc69787a346959c931568bd9e2cc4, accessed on 02/Apr/2020).

In addition to the epidemiological situation, the city of Rio de Janeiro presents sharp social inequalities in housing, income, and demographics 66. Ribeiro MG. Território e desigualdades de renda em regiões metropolitanas do Brasil. Dados 2015; 58:913-50., creating the urgent need for surveillance to identify areas with greater vulnerability to the severe form of COVID-19, with the aim of optimizing control of its spread and prevention of severe cases. Studies have pointed to the impact in population groups disproportionately exposed to the risk of respiratory diseases based on their living conditions and health status 77. Redefining vulnerability in the era of COVID-19. Lancet 2020; 395:1089.,88. Associação Brasileira de Saúde Coletiva. Especial coronavírus. Como se dará a evolução de Covid-19 na população que vive em condições precárias. https://www.abrasco.org.br/site/outras-noticias/opiniao/como-se-dara-a-evolucao-de-covid-19-na-populacao-que-vive-em-condicoes-precarias/46286/ (acessado em Abr/2020).https://www.abrasco.org.br/site/outras-n... .

The current study thus aims to characterize the areas of the city of Rio de Janeiro according to their vulnerability to the severe form of COVID-19, as factors that increase transmission of the infection and the severity of cases.

Methodology

Study area

The city of Rio de Janeiro, capital of the state by the same name, is located in the Southeast region of Brazil. The city’s area is approximately 1,197km², and the population was 6,320,446 in 2010. The city is divided into 10 Planning Areas, 33 Administrative Regions, and 160 neighborhoods. Rio displays enormous geographic complexity (topographic characteristics, peculiarities of the coastline, and spatial heterogeneity of land use and occupation), making this urban territory a mosaic of landscapes and social contrasts (Figure 1).

Figure 1Administrative division of the city of Rio de Janeiro, Brazil.

Vulnerability to the severe form of COVID-19 was measured by elaborating a composite index, calculated by cross-referencing three levels of information pertaining to indicators that increase the transmission and the severity of cases. Information was used on the mean number of residents per household per census tract, density of persons 60 years or older per km² per census tract, and TB incidence per 100,000 inhabitants per neighborhood. The TB incidence rate jointly expresses the presence of housing contexts favorable to the transmission of respiratory diseases and a risk factor for severe forms of COVID-19 99. San Pedro A, Gibson G, Santos JPC, Toledo LM, Sabroza PC, Oliveira RM. Tuberculose como marcador de iniquidades em um contexto de transformações socioespaciais. 2017. Rev Saúde Pública 2017; 51:9.,1010. Maciel ELN, Gonçalves Júnior E, Dalcolmo MMP. Tuberculose e coronavírus: o que sabemos? Epidemiol Serv Saúde 2020; 29:e2020128.. Since the study aimed to develop an indicator for timely operationalization and rapid response in crisis situations, we opted to create a simplified model (with few variables) rather than a more complex model.

The mean number of residents per household used data from the 2010 Population Census to calculate the resident population divided by the number of households 1111. Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2010: resultados do universo por setor censitário, 2011. https://censo2010.ibge.gov.br/ (acessado em Abr/2020).https://censo2010.ibge.gov.br/... . Higher numbers of residents per household were considered a facilitating factor for COVID-19 transmission, considering exposure to the viral load between susceptible and infected individuals within the household.

To build the indicator “density of persons 60 years or older per km2” in 2020, the database of the 2010 Population Census was used to obtain the number of persons over 50 years of age, and this total was divided by the occupied residential area in each census tract 1212. Santos JPC, Honório NA, Nobre AA. Definition of persistent areas with increased dengue risk by detecting clusters in populations with differing mobility and immunity in Rio de Janeiro, Brazil. Cad Saúde Pública 2019; 35:e00248118.. Mapping the area actually occupied in the territory was done with “Supervised Classification” of the Landsat 8 satellite image from 2018 (https://landsat.gsfc.nasa.gov/landsat-data-continuity-mission/). The semiautomatic classification was refined by visual interpretation of the Pleiades satellite image (year 2018. https://eos.com/pleiades-1/), which consists of manual vectorization of the target classes. The density of persons over 60 years of age expresses greater density of a risk group for the development of the severe form of COVID-19 1313. Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA 2020; [Epub ahead of print]..

The TB incidence rate per 100,000 inhabitants according to neighborhoods in the year 2018 was calculated with data on new cases of pulmonary TB provided by the Brazilian Information System for Notificable Diseases (SINAN) and the population projections for each neighborhood (Secretaria Municipal de Saúde do Rio de Janeiro. TabNet Linux 2.6a: tuberculose - SINAN Net. http://tabnet.rio.rj.gov.br/cgi-bin/dh?sinan/definicoes/tuberc2007.def, accessed on 03/Apr/2020). This level of information jointly expresses the occurrence of spaces prone to transmission of respiratory etiological agents and more precarious socioeconomic conditions 99. San Pedro A, Gibson G, Santos JPC, Toledo LM, Sabroza PC, Oliveira RM. Tuberculose como marcador de iniquidades em um contexto de transformações socioespaciais. 2017. Rev Saúde Pública 2017; 51:9..

Having built and mapped the information levels, analytic hierarchy process (AHP) 1414. Saraiva VI, Silva AS, Santos JPC. Suscetibilidade à erosão dos solos da bacia hidrográfica lagos - São João, no Estado do Rio de Janeiro - Brasil, a partir do método AHP e análise multicritério. Revista Brasileira de Geografia Física 2019; 12:1415-30. was used to determine each level’s relative contribution to the data integration and construction of the index. The levels’ percentage contribution to vulnerability to severe COVID-19 was 40% for TB, 30% for density of persons 60 years or older, and 30% for mean residents per household. The information levels were standardized by the minimax method such that they varied from 0 to 1 in order for the different scales of magnitude not to interfere with construction of the index.

Based on this standardization and the definition of the relative contributions, thematic integration of the different levels of information was performed through weighted linear combination via map algebra 1515. Jiang H, Eastman JR. Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science 2000; 14:173-84..

After the thematic integration process, the map was obtained that expresses, in the territory, the index of vulnerability to the severe form of COVID-19 in the city of Rio de Janeiro on the census-tract scale, analyzed on different geographic scales such as Administrative Regions and neighborhoods. All the data processing and mapping were performed in ArcGis 10.5 (http://www.esri.com/software/arcgis/index.html).

Table 1 Mean number of residents per household, density of persons 60 years and older, tuberculosis (TB) incidence, and classification of vulnerability to severe COVID-19 according to Administrative Regions of Rio de Janeiro, Brazil.

Figure 3Spatial distribution of density of persons 60 years and older according to census tracts in the city of Rio de Janeiro, Brazil.

Spatial distribution of TB shows very high incidence rates in a large share of the neighborhoods in the city of Rio de Janeiro, especially those comprising the Administrative Regions of Jacarezinho: 645/100,000, Cidade de Deus: 527/100,000, Inhaúma: 475/100,000, Portuária: 428/100,000, Complexo da Maré: 410/100,000, Complexo do Alemão: 396/100,000, Bangu: 364/100,000, Ramos: 316/100,000, and Rocinha: 313/100,000 (Figure 4, Table 1).

The characterization of vulnerability to severe COVID-19 stratified the municipality into five classes: (1) very low vulnerability, with 41.9% of the city’s occupied area and 16.6% of the population; (2) low, with 13.4% of the occupied area and 20.5% of the population; (3) medium, with 17.4% of the occupied area and 21.1% of the population; (4) high, with 18.2% of the occupied area and 20.2% of the population; and (5) very high, accounting for 8.9% of the city’s occupied area and 21.3% of the population. The census tracts classified as having high and very high vulnerability are located in different Administrative Regions of the city, especially Bangu, Guaratiba, Cidade de Deus, Rocinha, Copacabana, Rio Comprido, São Cristóvão, Ramos, Inhaúma, Penha, and Vigário Geral (Figure 5).

Figure 5Spatial distribution of vulnerability to the severe form of COVID-19 according to Administrative Regions in Rio de Janeiro, Brazil.

Discussion

The findings reveal a highly heterogeneous spatial pattern in terms of vulnerability to the severe form of COVID-19 in Rio de Janeiro, with more vulnerable areas spread across the entire territory and reflecting its urban complexity. However, the results also show that the areas with greatest vulnerability are located in the North Zone and the non-coastal part of the West Zone and poor communities nested within wealthy areas (the coastal parts of the South Zone and West Zone), such as Rocinha and Cidade de Deus, respectively.

The elaboration of the vulnerability index used a simplified number of information levels that could express processes associated with higher possibility of transmission as well as living and demographic conditions related to the severe form of COVID-19.

One limitation to the study was the spatial modeling based on data from the 2010 Population Census. Despite the time lag, this is the principal source of information on territorial scales with the smallest level of aggregation. In order to minimize the time lag effect, we defined as the population 60 years and older in 2020 the persons who were 50 years or older at the time of the 2010 Population Census. The magnitude of household density may vary over time, but its differential pattern tends to be maintained. As for TB incidence, the decision to use data from 2018 was due to the time needed to consolidate the data in the information system.

Considering the current moment in the pandemic, the proposal for a simplified indicator for vulnerability to the severe form of COVID-19 is justified by the urgent need to develop surveillance and clinical care strategies that take into account the spatial distribution of specific aspects the occurrence of COVID-19 in each territory. Specifically, in terms of clinical care, the indicator can help orient activities in prevention and patient care by the Family Health Program, based on community needs identified with the indicator 1616. Vitória AM, Campos GWS. Só com APS forte o sistema pode ser capaz de achatar a curva de crescimento da pandemia e garantir suficiência de leitos UTI. http://www.cosemssp.org.br/noticias/dicadogestor-so-com-aps-forte-o-sistema-pode-ser-capaz-de-achatar-a-curva-de-crescimento-da-pandemia-e-garantir-suficiencia-de-leitos-uti/ (acessado em 02/Abr/2020).http://www.cosemssp.org.br/noticias/dica... .

Table 1 Mean number of residents per household, density of persons 60 years and older, tuberculosis (TB) incidence, and classification of vulnerability to severe COVID-19 according to Administrative Regions of Rio de Janeiro, Brazil.

Figure 1 Administrative division of the city of Rio de Janeiro, Brazil.

Source: Brazilian Institute of Geography and Statistics.

Figure 2 Spatial distribution of mean number of persons per household according to census tracts in the city of Rio de Janeiro, Brazil.

Figure 5 Spatial distribution of vulnerability to the severe form of COVID-19 according to Administrative Regions in Rio de Janeiro, Brazil.

Table 1 Mean number of residents per household, density of persons 60 years and older, tuberculosis (TB) incidence, and classification of vulnerability to severe COVID-19 according to Administrative Regions of Rio de Janeiro, Brazil.